IMHE OpenIR  > 山地表生过程与生态调控重点实验室
Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data
Tang, Ying1,2; Winkler, Julie A.1; Vina, Andres2,3; Wang, Fang2; Zhang, Jindong2; Zhao, Zhiqiang2; Connor, Thomas2; Yang, Hongbo2; Zhang, Yuanbin4; Zhang, Xiaofeng5; Li, Xiaohong6; Liu, Jianguo2
2020-03-01
Source PublicationECOLOGICAL INDICATORS
ISSN1470-160X
EISSN1872-7034
Volume110Pages:105891
SubtypeArticle
Contribution Rank4
AbstractOutputs of species distribution models (SDMs) are widely used as indicators of climate conditions favorable for species occurrence. When using these outputs to inform planning and decision making, it is essential that the uncertainties associated with the projections of present-day and future climatic suitability are carefully considered. Climate change assessments routinely consider the uncertainty introduced into SDM outputs by differences in future climate projections, and other uncertainty sources, such as the choice of the threshold to convert simulated probabilities to binary climatically suitable areas, are also oftentimes considered. However, the uncertainty introduced by the limitations of the species occurrence data used in the SDM calibration is rarely evaluated. These limitations, which include location error, sampling bias, and species misidentification, may reduce the utility of SDM outputs in conservation research and practice. Using understory bamboo species in southwest China as examples, here we demonstrate that species occurrences obtained using remote sensing offer an additional dataset for calibrating SDMs that, in conjunction with conventional observations and employing an ensemble approach of outputs from multiple models, provide an estimate of the uncertainty introduced by the species occurrence data. A biweekly time series of the satellite-based Wide Dynamic Range Vegetation Index (WDRI) was employed to estimate bamboo occurrence based on phenological signatures of the bamboo species and their overstory canopies. Using Maxent, a popular modeling framework, present-day and projected future climatic suitability were assessed separately for conventional species presence observations from the Fourth National Giant Panda Survey and for the remotely-sensed presence estimates. The ensemble of model outputs suggests that the uncertainty introduced by the species occurrence data, along with the interaction with other sources of uncertainty, may be as substantial as the uncertainty introduced by the use of different climate scenarios or by the threshold used to estimate binary climatically suitable areas. Ignoring the uncertainty introduced by the limitations of the species occurrences may compromise the interpretation of SDM outputs and reduce their usefulness for conservation planning. Remote sensing is a largely untapped resource for assessing uncertainty in SDM simulations.
KeywordClimate change Species distribution modeling Uncertainty Species occurrence data Remote sensing
DOI10.1016/j.ecolind.2019.105891
Indexed BySCI
WOS KeywordLAND-SURFACE PHENOLOGY ; DISTRIBUTION MODELS ; HABITAT ; PERFORMANCE ; IMPROVE ; BIAS ; DISTRIBUTIONS ; UNCERTAINTY ; PREDICTIONS ; PROJECTIONS
Language英语
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Environmental Sciences
WOS IDWOS:000507381800054
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/33752
Collection山地表生过程与生态调控重点实验室
Corresponding AuthorWinkler, Julie A.
Affiliation1.Michigan State Univ, Dept Geog Environm & Spatial Sci, Geog Bldg,637 Auditorium Rd, E Lansing, MI 48824 USA;
2.Michigan State Univ, Ctr Syst Integrat & Sustainabil, Dept Fisheries & Wildlife, E Lansing, MI 48824 USA;
3.Univ N Carolina, Dept Geog, Chapel Hill, NC 27515 USA;
4.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Sichuan, Peoples R China;
5.Shaanxi Forestry Dept, Xian, Shaanxi, Peoples R China;
6.Tianshui Normal Univ, Tianshui, Gansu, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Tang, Ying,Winkler, Julie A.,Vina, Andres,et al. Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data[J]. ECOLOGICAL INDICATORS,2020,110:105891.
APA Tang, Ying.,Winkler, Julie A..,Vina, Andres.,Wang, Fang.,Zhang, Jindong.,...&Liu, Jianguo.(2020).Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data.ECOLOGICAL INDICATORS,110,105891.
MLA Tang, Ying,et al."Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data".ECOLOGICAL INDICATORS 110(2020):105891.
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